Bigger things matter! Those are the entities that sow the seeds for biasing the results of the datasets, skewing their averages and giving the audience an illusion that is not even close to what exists in the reality. In this article, we will look into the sequence of steps involved in finding the maximum out of the given elements in the arrays using the *maximum( ) *function from the *numpy *library in Python. Before kicking things off, let us start with understanding its syntax.

*Also read: How to Use Numpy Minimum on Arrays?*

**Syntax of ***maximum( )* function

*maximum( )*function

Following are the basic constructs comprising both mandatory and optional elements that are to be provided for the effective functioning of the *maximum( ) *function from the *numpy *library.

```
numpy.maximum(x1, x2, out=None, *, where=True, dtype=None)
```

where,

input arrays holding the elements for which the maximum is to be found*x1, x2 â€“*an optional construct set to*out â€“**none*by default, but could be used to store the results in the desired array which is of the same length as the outputkwargs or keyword argument which is an optional construct used to pass keyword variable length of argument to a function***–an optional construct which is used to calculate the universal function (ufunc) at the given position when set to*where â€“**True*(default setting) or not calculate when set to*False*an optional construct used to specify the data type which is being used*dtype â€“*

**Using ***maximum( ) *on One Dimensional Arrays

*maximum( )*on One Dimensional Arrays

Let us kick things off by importing the *numpy *library within Python by using the following code.

```
import numpy as np
```

Now let us construct a couple of one dimensional arrays for which the maximum elements are to be determined.

```
ar1 = np.array([[1.2, 3.4, 6.7, 8.9]], dtype = int)
ar2 = np.array([[2.1, 4.3, 5.7, 6.9]], dtype = int)
```

It could be noticed above that the data type being considered is *int *so one can very well expect the output to be stripped off the decimal numbers. Now it is time to use the *maximum( ) *function!

```
np.maximum(ar1, ar2, dtype = int)
```

**Using ***maximum( ) *on N-Dimensional Arrays

*maximum( )*on N-Dimensional Arrays

Now that we have dealt with finding the maximum amongst the elements of one-dimensional arrays, let us in this section extend our quest by using arrays of multiple dimensions such as those given below to return their maximum elements using the *maximum( ) *function.

```
ar1 = np.array([[1.2, 3.4, 6.7, 8.9],
[9.8, 7.6, 5.4, 3.2]], dtype = float)
ar2 = np.array([[2.1, 4.3, 5.7, 6.9],
[9.7, 8.6, 4.5, 1.2]], dtype = float)
np.maximum(ar1, ar2, dtype = float)
```

The trained eyes could be observant of the similarity with the output array of the previous section that both of them are of the same dimensions as their input arrays. This gives us a clue on how to construct an output array if at all, we would like to store the result elsewhere.

**Using ***where *in *maximum( ) *function

*where*in

*maximum( )*function

We now get to the great part of using the *maximum( ) *function in which one can also selectively find the maximum of the given array elements confined to only a particular position within the array by exercising the *where *option as demonstrated below.

```
ar1 = np.array([[1.2, 3.4, 6.7, 8.9],
[9.8, 7.6, 5.4, 3.2]], dtype = float)
ar2 = np.array([[2.1, 4.3, 5.7, 6.9],
[9.7, 8.6, 4.5, 1.2]], dtype = float)
np.maximum(ar1, ar2, where = [True, True, False, True])
```

The above code asks the *maximum( ) *function to only return the maximum values of the input arrays compared in all the positions except for the third where it runs the contrarian execution.

**Conclusion:**

Now that we have reached the end of this article, hope it has elaborated on how to find the maximum of array elements using the *maximum( ) *function of the *numpy *library. Hereâ€™s another article that explains how to find the minimum of array elements using *numpy *in Python. There are numerous other enjoyable & equally informative articles in AskPython that might be of great help to those who are looking to level up in Python. Whilst you enjoy those,Â *hasta luego*!